Research on the online evaluation of the straightness error of hydrostatic guideways based on deep learning.

Currently, the measurement of guide rail straightness error is basically a direct measurement, which cannot meet the requirements of online straightness error measurement in the machining process of the machine tool. Therefore, this paper proposes a straightness error evaluation model based on the recess pressure, which can realize the online measurement of the straightness error of hydrostatic guideways. To address the nonlinear relationship between the recess pressure and the linear deviation data of the guideway in an experiment, based on deep learning, the hydrostatic guideway straightness error was evaluated. First, the experimental data are analyzed and processed by feature, and a sliding window is processed using the data time sequence. Second, a long short-term memory network model is constructed based on an attention mechanism, the model parameters are obtained through orthogonal experiments, and the theoretical straightness error of the hydrostatic guideway is obtained via training. Finally, the theoretical values and experimental values of straightness error are compared and evaluated with the multilayer perceptron and recurrent neural network models. The results show that the model can effectively evaluate the straightness error of hydrostatic guideways. [ABSTRACT FROM AUTHOR]